On Sat, 10 May 2003 23:59:40 -0400
Osman Al-Radi <
osman.al.radi@utoronto.ca>
wrote:
>
> Dear Prof. Harrell,
>
> Thank you for
your quick response (even during the weekend)..
>
> I do not want
to stratify by treatment (rx) because the model in this case
> dose not
allow inferences about rx which is the aim of the study..
>
> The
second option in Therneau and Grambsch is to partition the follow-up
>
time.. essentially get two or more models.. Unfortunately this reduces
the
> number of events in each phase of follow-up and I can not afford
that in
> terms of the number of dfs I need to adjust for baseline
differences in this
> observational study..
>
> The third
option which is the basis of my question is to model the
>
non-proportionality by including a time-dependent covariate.. here the
T&G
> book suggest using SAS PHREG to include such a variable (a
function of the
> survival time).. I may have to do that but I was
wondering if there is a way
> of doing that in S (I do not believe that is
described in the book)
>
> I am grateful for your help..
>
> Osman
>
>
Inference will not be easy for the
effect of rx if you use time-dependent covariates. And depending on how
you slice the time axis and model the interaction between time and rx, the
"time-slicing" approach using the coxph function will have the same spending of
d.f. and increase in variance as the continuous time interaction method that is
easy to do with SAS PHREG. Note also that you can have continuous time
interactions with coxph; it will just require the creation of a large
dataset.
Frank
>
>
>
>
> ----- Original
Message -----
> From: "Frank E Harrell Jr" <
fharrell@virginia.edu>
> To:
"Osman Al-Radi" <
osman.al.radi@utoronto.ca>
>
Cc: <
s-news@lists.biostat.wustl.edu>
>
Sent: Saturday, May 10, 2003 9:06 PM
> Subject: Re: [S] non-proportioana
hazards for the treatment effect
>
>
> > On Sat, 10 May
2003 12:29:14 -0400
> > Osman Al-Radi <
osman.al.radi@utoronto.ca>
wrote:
> >
> > > Hi
> > >
> > >
f<-cph(Surv(time.status)~age+sex+dm+rx,x=T,y=T)
> > >
>
> > The main treatment variable (rx) fails the PH assumption test
of
> cox.zph() in a cph() model..
> > >
> > > I
would like to model the non-proportional effect..
> > >
> >
> If I include rx:time interaction the model fails to converge!
> >
>
> > > How can I model the non-proportionality ? I do not want
to subset by
> time because this results in two or more models with less
events in each and
> I do not have enough dfs..
> > >
>
> > I would appreciate any suggestions..
> > >
> >
> Osman
> >
> > When posting to S-news it is best to speak
of the most basic functions.
> As cph (in the Design library) uses coxph
(in the survival library) it would
> be better to state you question more
generally using coxph.
> >
> > The simplest way to allow for
non-proportional hazards for a categorical
> variable such as rx is to
stratify on it (strata(rx) for coxph, strat(rx)
> for cph, an unfortunate
complication). And note that if you do not want to
> use
stratification, you can't just interact follow-up time with a covariate
>
if you want to use time-dependent covariates. See coxph documentation
or
> the Therneau & Grambsch book for how to do that.
>
>
> > ---
> > Frank E Harrell
Jr
Prof. of Biostatistics & Statistics
> > Div. of Biostatistics &
Epidem. Dept. of Health Evaluation Sciences
> > U. Virginia School of
Medicine
http://hesweb1.med.virginia.edu/biostat>
>
>
---
Frank E Harrell
Jr
Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem.
Dept. of Health Evaluation Sciences
U. Virginia School of Medicine
http://hesweb1.med.virginia.edu/biostat--------------------------------------------------------------------
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